Computers have historically been used in a variety of manners to supplement, augment, or replace human capabilities in performing various tasks. Examples include performing otherwise-burdensome or time-consuming calculations, providing for the storage and querying of large amounts of data, and providing visualizations to enable graphical manipulation of data.
One arena in which people have sought to obtain assistance from computers is decision-making, e.g., classifying, identifying, reasoning about, or predicting a nature of known or future data. Related technical fields are known to include, e.g., artificial intelligence (AI), or machine learning. Such technical fields are challenging, because they seek to recreate, on a large scale, the types of subjective, irregular, non-linear, and/or nuanced distinctions that human beings routinely make, which are not typically solvable simply through the manipulation of large amounts of data and/or high processing speeds.
In order to implement these types of technology, machine learning algorithms and associated machine learning models have been developed. However, within the various technical fields in which such technology is deployed, such algorithms and models have had varying degrees of success, and, moreover, different technical fields may experience more or less success with a given machine learning algorithm than another machine learning algorithm. Further, even when a suitable machine learning algorithm is known to exist for a corresponding type of decision-making problem, the machine learning algorithm must be configured correctly in order to provide acceptable outcomes.
In short, it is difficult to select and configure a machine learning algorithm to obtain desired outcomes within an acceptable time period, and/or with an acceptable level of resources consumed. As a result, potential users often do not obtain the benefits of such machine learning algorithms.